Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-5105-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5105-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2021
Research article |
| 22 Sep 2021
| 22 Sep 2021
A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations
Robert Ljubičić
CORRESPONDING AUTHOR
Department of Hydraulic and Environmental Engineering, Faculty of
Civil Engineering, University of Belgrade, Belgrade 11120, Serbia
Dariia Strelnikova
School of Geoinformation, Carinthia University of Applied Sciences, Villach 9524, Austria
Matthew T. Perks
School of Geography, Politics and Sociology, Newcastle University,
Newcastle upon Tyne NE1 7RU, United Kingdom
Anette Eltner
Institute of Photogrammetry and Remote Sensing, Technische
Universität Dresden, 01069 Dresden, Germany
Salvador Peña-Haro
Photrack AG, Ankerstrasse 16a, 8004 Zurich, Switzerland
Alonso Pizarro
Escuela de Ingeniería en Obras Civiles, Universidad Diego
Portales, 8370109 Santiago, Chile
Silvano Fortunato Dal Sasso
Department of European and Mediterranean Cultures: Architecture,
Environment and Cultural Heritage (DICEM), University of Basilicata, 75100
Matera, Italy
Ulf Scherling
School of Geoinformation, Carinthia University of Applied Sciences, Villach 9524, Austria
Pietro Vuono
Department of Civil, Architectural and Environmental Engineering,
University of Naples Federico II, 80125 Naples, Italy
Salvatore Manfreda
Department of Civil, Architectural and Environmental Engineering,
University of Naples Federico II, 80125 Naples, Italy
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Seyed Mohammad Hassan Erfani, Corinne Smith, Zhenyao Wu, Elyas Asadi Shamsabadi, Farboud Khatami, Austin R. J. Downey, Jasim Imran, and Erfan Goharian
Hydrol. Earth Syst. Sci., 27, 4135–4149, https://doi.org/10.5194/hess-27-4135-2023, https://doi.org/10.5194/hess-27-4135-2023, 2023
Short summary
Short summary
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Ravi Kumar Meena, Sumit Sen, Aliva Nanda, Bhargabnanda Dass, and Anurag Mishra
Hydrol. Earth Syst. Sci., 26, 4379–4390, https://doi.org/10.5194/hess-26-4379-2022, https://doi.org/10.5194/hess-26-4379-2022, 2022
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Short summary
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S. B. Shaw and M. T. Walter
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Cited articles
Abdullah, L. M., Tahir, N. M., and Samad, M.: Video stabilization based on
point feature matching technique, Proc. – 2012 IEEE Control Syst. Grad. Res.
Colloquium, ICSGRC 2012, (Icsgrc), 303–307,
https://doi.org/10.1109/ICSGRC.2012.6287181, 2012.
Aguilar, W. G. and Angulo, C.: Real-time video stabilization without phantom
movements for micro aerial vehicles, Eurasip J. Image Video Process.,
2014, 1–13, https://doi.org/10.1186/1687-5281-2014-46, 2014a.
Aguilar, W. G. and Angulo, C.: Robust video stabilization based on motion
intention for low-cost micro aerial vehicles, 2014 IEEE 11th Int.
Multi-Conference Syst. Signals Devices, SSD 2014, 1–6,
https://doi.org/10.1109/SSD.2014.6808863, 2014b.
Aguilar, W. G. and Angulo, C.: Real-Time Model-Based Video Stabilization for
Microaerial Vehicles, Neural Process. Lett., 43, 459–477,
https://doi.org/10.1007/s11063-015-9439-0, 2016.
Alcantarilla, P., Nuevo, J., and Bartoli, A.: Fast Explicit Diffusion for
Accelerated Features in Nonlinear Scale Spaces, in Procedings of the British
Machine Vision Conference 2013, British Machine Vision
Association, 13.1–13.11, https://doi.org/10.5244/C.27.13, 2013.
Auysakul, J., Xu, H., and Pooneeth, V.: A hybrid motion estimation for video
stabilization based on an IMU sensor, Sensors (Switzerland), 18, 2708,
https://doi.org/10.3390/s18082708, 2018.
Baek, D., Seo, I. W., Kim, J. S., and Nelson, J. M.: UAV-based measurements
of spatio-temporal concentration distributions of fluorescent tracers in
open channel flows, Adv. Water Resour., 127, 76–88,
https://doi.org/10.1016/j.advwatres.2019.03.007, 2019.
Battiato, S., Gallo, G., Puglisi, G., and Scellato, S.: SIFT Features
Tracking for Video Stabilization, in 14th International Conference on Image
Analysis and Processing (ICIAP 2007), IEEE,
825–830, https://doi.org/10.1109/ICIAP.2007.4362878, 2007.
Battiato, S., Puglisi, G., and Bruna, A. R.: A robust video stabilization
system by adaptive motion vectors filtering, in 2008 IEEE International
Conference on Multimedia and Expo, IEEE, 373–376,
https://doi.org/10.1109/ICME.2008.4607449, 2008.
Batur, A. U. and Flinchbaugh, B.: Video stabilization with optimized motion
estimation resolution, Proc. – Int. Conf. Image Process. ICIP, 465–468,
https://doi.org/10.1109/ICIP.2006.312494, 2006.
Blender Online Community: Blender – a 3D modelling and rendering package, Stichting Blender Foundation, Amsterdam, available at: https://www.blender.org, last access: 21 February 2021.
Censi, A., Fusiello, A., and Roberto, V.: Image stabilization by features
tracking, Proc. – Int. Conf. Image Anal. Process. ICIAP 1999, 1, 665–669,
https://doi.org/10.1109/ICIAP.1999.797671, 1999.
Chang, H.-C., Lai, S.-H., and Lu, K.-R.: A robust and efficient video
stabilization algorithm, in IEEE International Conference on Multimedia and
Expo (ICME), IEEE Cat. No.04TH8763, IEEE, 29–32,
https://doi.org/10.1109/ICME.2004.1394117, 2004.
Choi, S., Kim, T., and Yu, W.: Robust video stabilization to outlier motion
using adaptive RANSAC, in 2009 IEEE/RSJ International Conference on
Intelligent Robots and Systems, IEEE, 1897–1902,
https://doi.org/10.1109/IROS.2009.5354240, 2009.
Dal Sasso, S. F., Pizarro, A., and Manfreda, S.: Metrics for the
Quantification of Seeding Characteristics to Enhance Image Velocimetry
Performance in Rivers, Remote Sens., 12, 1789,
https://doi.org/10.3390/rs12111789, 2020.
Deng, Z., Yang, D., Zhang, X., Dong, Y., Liu, C., and Shen, Q.: Real-Time
Image Stabilization Method Based on Optical Flow and Binary Point Feature
Matching, Electronics, 9, 198,
https://doi.org/10.3390/electronics9010198, 2020.
Detert, M. and Weitbrecht, V.: Helicopter-based surface PIV experiments at
Thur River, in River Flow 2014, CRC Press, 2003–2008,
https://doi.org/10.1201/b17133-267, 2014.
Detert, M. and Weitbrecht, V.: A low-cost airborne velocimetry system: Proof
of concept, J. Hydraul. Res., 53, 532–539,
https://doi.org/10.1080/00221686.2015.1054322, 2015.
Detert, M., Johnson, E. D., and Weitbrecht, V.: Proof-of-concept for low-cost
and non-contact synoptic airborne river flow measurements, Int. J. Remote
Sens., 38, 2780–2807, https://doi.org/10.1080/01431161.2017.1294782,
2017.
Eltner, A.: FlowVeloTool, GitHub [code], available at: https://github.com/AnetteEltner/FlowVeloTool, last access: 21 February 2021.
Eltner, A., Sardemann, H., and Grundmann, J.: Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery, Hydrol. Earth Syst. Sci., 24, 1429–1445, https://doi.org/10.5194/hess-24-1429-2020, 2020.
Engelsberg, A. and Schmidt, G.: A comparative review of digital image
stabilising algorithms for mobile video communications, in Digest of
Technical Papers. International Conference on Consumer Electronics (Cat.
No.99CH36277), IEEE, 88–89, https://doi.org/10.1109/ICCE.1999.785180,
1999.
Ertürk, S.: Real-time digital image stabilization using Kalman filters,
Real-Time Imaging, 8, 317–328, https://doi.org/10.1006/rtim.2001.0278,
2002.
Ertürk, S.: Digital image stabilization with sub-image phase correlation
based global motion estimation, IEEE Trans. Consum. Electron., 49,
1320–1325, https://doi.org/10.1109/TCE.2003.1261235, 2003.
Fischler, M. A. and Bolles, R. C.: Random sample consensus: a paradigm for
model fitting with applications to image analysis and automated cartography,
Commun. ACM, 24, 381–395, https://doi.org/10.1145/358669.358692, 1981.
Fujita, I. and Notoya, Y.: Development of Uav-Based River Surface Velocity
Measurement By Stiv Based on High-Accurate Image Stabilization Techniques,
E-proceedings 36th IAHR World Congr., 28 June–3 July 2015, Hague,
Netherlands Dev., 1, 1–10, 2015.
Grundmann, M., Kwatra, V., and Essa, I.: Auto-directed video stabilization
with robust L1 optimal camera paths, in: CVPR 2011, IEEE, 225–232,
https://doi.org/10.1109/CVPR.2011.5995525, 2011.
Hanning, G., Forslow, N., Forssen, P.-E., Ringaby, E., Tornqvist, D., and
Callmer, J.: Stabilizing cell phone video using inertial measurement
sensors, in 2011 IEEE International Conference on Computer Vision Workshops
(ICCV Workshops), IEEE, 1–8,
https://doi.org/10.1109/ICCVW.2011.6130215, 2011.
Hong, S., Hong, T., and Yang, W.: Multi-resolution unmanned aerial vehicle
video stabilization, Proc. IEEE 2010 Natl. Aerosp. Electron. Conf. NAECON
2010, 126–131, https://doi.org/10.1109/NAECON.2010.5712935, 2010.
Hu, R., Shi, R., Shen, I. F., and Chen, W.: Video stabilization using
scale-invariant features, Proc. Int. Conf. Inf. Vis., 871–876,
https://doi.org/10.1109/IV.2007.119, 2007.
Kejriwal, L. and Singh, I.: A Hybrid Filtering Approach of Digital Video
Stabilization for UAV Using Kalman and Low Pass Filter, Procedia Comput.
Sci., 93, 359–366,
https://doi.org/10.1016/j.procs.2016.07.221, 2016.
Kwon, O., Shin, J., and Paik, J.: Video stabilization using Kalman filter and
phase correlation matching, Lect. Notes Comput. Sci. (including Subser.
Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 3656 LNCS, 141–148,
https://doi.org/10.1007/11559573_18, 2005.
Le Boursicaud, R., Pénard, L., Hauet, A., Thollet, F., and Le Coz, J.:
Gauging extreme floods on YouTube: application of LSPIV to home movies for
the post-event determination of stream discharges, Hydrol. Process., 30,
90–105, https://doi.org/10.1002/hyp.10532, 2016.
Le Coz, J., Patalano, A., Collins, D., Guillén, N. F., Garciá, C.
M., Smart, G. M., Bind, J., Chiaverini, A., Le Boursicaud, R., Dramais, G.,
and Braud, I.: Lessons learnt from recent citizen science initiatives to
document floods in France, Argentina and New Zealand, E3S Web Conf., 7,
6–11, https://doi.org/10.1051/e3sconf/20160716001, 2016.
Lewis, Q. W., Lindroth, E. M., and Rhoads, B. L.: Integrating unmanned aerial
systems and LSPIV for rapid, cost-effective stream gauging, J. Hydrol., 560,
230–246, https://doi.org/10.1016/j.jhydrol.2018.03.008, 2018.
Liberzon, A., Lasagna, D., Aubert, M., Bachant, P., Mahmoodtabar, E.,
Käufer, T., jakirkham, Bauer, A., Vodenicharski, B., Dallas, C., Yang,
E., Borg, J., Farzan, M. M., tomerast and ranleu: OpenPIV/openpiv-python:
OpenPIV-Python v0.22.3, Zenodo, https://doi.org/10.5281/ZENODO.4042115, 2020.
Lim, A., Ramesh, B., Yang, Y., Xiang, C., Gao, Z., and Lin, F.: Real-time
optical flow-based video stabilization for unmanned aerial vehicles, J.
Real-Time Image Process., 16, 1975–1985,
https://doi.org/10.1007/s11554-017-0699-y, 2019.
Litvin, A., Konrad, J., and Karl, W. C.: Probabilistic video stabilization
using Kalman filtering and mosaicing, Image Video Commun. Process. 2003,
5022, 663, https://doi.org/10.1117/12.476436, 2003.
Liu, F., Gleicher, M., Jin, H., and Agarwala, A.: Content-preserving warps
for 3D video stabilization, ACM Trans. Graph., 28, 1–9,
https://doi.org/10.1145/1531326.1531350, 2009.
Liu, F., Gleicher, M., Wang, J., Jin, H., and Agarwala, A.: Subspace video
stabilization, ACM Trans. Graph., 30, 4,
https://doi.org/10.1145/1899404.1899408, 2011.
Liu, S., Wang, Y., Yuan, L., Bu, J., Tan, P., and Sun, J.: Video
stabilization with a depth camera, in: 2012 IEEE Conference on Computer
Vision and Pattern Recognition, IEEE, 89–95,
https://doi.org/10.1109/CVPR.2012.6247662, 2012.
Liu, S., Yuan, L., Tan, P., and Sun, J.: Bundled camera paths for video
stabilization, ACM Trans. Graph., 32, 78,
https://doi.org/10.1145/2461912.2461995, 2013.
Ljubičić, R.: SSIMS: SSIM-based digital image stabilization suite, GitHub [code], available at: https://github.com/ljubicicrobert/SSIMS, last access: 21 February 2021.
Ljubičić, R., Strelnikova, D., Perks, M. T., Eltner, A., Peña-Haro, S., Pizarro, A., Dal Sasso, S. F., Scherling, U., Vuono, P., and Manfreda, S.: Video stabilisation results obtained using different tools for UAS-based image velocimetry (v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4557921, 2021.
Lowe, D. G.: Distinctive Image Features from Scale-Invariant Keypoints, Int.
J. Comput. Vis., 60, 91–110,
https://doi.org/10.1023/B:VISI.0000029664.99615.94, 2004.
Mai, Y., Zhao, H., and Guo, S.: The analysis of image stabilization
technology based on small-UAV airborne video, Proc. – 2012 Int. Conf.
Comput. Sci. Electron. Eng. ICCSEE 2012, 3, 586–589,
https://doi.org/10.1109/ICCSEE.2012.77, 2012.
Manfreda, S., McCabe, M., Miller, P., Lucas, R., Pajuelo Madrigal, V.,
Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G.,
Müllerová, J., Tauro, F., de Lima, M., de Lima, J., Maltese, A.,
Frances, F., Caylor, K., Kohv, M., Perks, M., Ruiz-Pérez, G., Su, Z.,
Vico, G., and Toth, B.: On the Use of Unmanned Aerial Systems for
Environmental Monitoring, Remote Sens., 10, 641,
https://doi.org/10.3390/rs10040641, 2018.
Manfreda, S., Dal Sasso, S. F., Pizarro, A., and Tauro, F.: Chapter 10: New
Insights Offered by UAS for River Monitoring, in Applications of Small
Unmanned Aircraft Systems: Best Practices and Case Studies, CRC Press,
Taylor & Francis Grous, 2019.
Marcenaro, L., Vernazza, G., and Regazzoni, C. S.: Image stabilization
algorithms for video-surveillance applications, in Proceedings 2001
International Conference on Image Processing (Cat. No.01CH37205), IEEE, 1,
349–352, https://doi.org/10.1109/ICIP.2001.959025, 2001.
MathWorks: What Is Camera Calibration?, available at:
https://www.mathworks.com/help/vision/ug/camera-calibration.html, last access:
19 May 2021a.
MathWorks: Camera Calibrator,
available at: https://www.mathworks.com/help/vision/ug/single-camera-calibrator-app.html,
last access: 19 May 2021b.
Matsushita, Y., Ofek, E., Tang, X., and Shum, H. Y.: Full-frame video
stabilization, Proc. – 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern
Recognition, CVPR 2005, I, 50–57, https://doi.org/10.1109/CVPR.2005.166,
2005.
Morimoto, C. and Chellappa, R.: Fast electronic digital image stabilization,
Proc. – Int. Conf. Pattern Recognit., 3, 284–288,
https://doi.org/10.1109/ICPR.1996.546956, 1996a.
Morimoto, C. and Chellappa, R.: Fast Electronic Digital Image Stabilization
for Off-Road Navigation, Real-Time Imaging, 2, 285–296,
https://doi.org/10.1006/rtim.1996.0030, 1996b.
Morimoto, C. and Chellappa, R.: Evaluation of image stabilization
algorithms, in: Proceedings of the 1998 IEEE International Conference on
Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), IEEE,
5, 2789–2792, https://doi.org/10.1109/ICASSP.1998.678102,
1998.
Niskanen, M., Silven, O., and Tico, M.: Video Stabilization Performance
Assessment, in 2006 IEEE International Conference on Multimedia and Expo, IEEE,
405–408, https://doi.org/10.1109/ICME.2006.262522, 2006.
Odelga, M., Kochanek, N., and Bülthoff, H. H.: Efficient real-time video stabilization for UAVs using only IMU data, 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), 210–215, https://doi.org/10.1109/RED-UAS.2017.8101668, 2017.
OpenCV: Camera calibration With OpenCV, Open Source Computer Vision,
available at: https://docs.opencv.org/master/d4/d94/tutorial_camera_calibration.html, last access: 19 May 2021.
Pearce, S., Ljubičić, R., Peña-Haro, S., Perks, M., Tauro, F.,
Pizarro, A., Dal Sasso, S., Strelnikova, D., Grimaldi, S., Maddock, I.,
Paulus, G., Plavšić, J., Prodanović, D., and Manfreda, S.: An
Evaluation of Image Velocimetry Techniques under Low Flow Conditions and
High Seeding Densities Using Unmanned Aerial Systems, Remote Sens., 12,
232, https://doi.org/10.3390/rs12020232, 2020.
Peña-Haro, S.: FFTVidStabilization, GitHub [code], available at: https://github.com/salpeha/FFTVidStabilization, last access: 21 February 2021.
Perks, M. T.: KLT-IV v1.0: image velocimetry software for use with fixed and mobile platforms, Geosci. Model Dev., 13, 6111–6130, https://doi.org/10.5194/gmd-13-6111-2020, 2020.
Perks, M. T.: KLT-IV, SourceForge [code], available at: https://sourceforge.net/projects/klt-iv (last access: 21 February 2021), 2020.
Perks, M. T., Russell, A. J., and Large, A. R. G.: Technical Note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs), Hydrol. Earth Syst. Sci., 20, 4005–4015, https://doi.org/10.5194/hess-20-4005-2016, 2016.
Pinto, B. and Anurenjan, P. R.: Video stabilization using Speeded Up Robust
Features, ICCSP 2011–2011 Int. Conf. Commun. Signal Process., 527–531,
https://doi.org/10.1109/ICCSP.2011.5739378, 2011.
Pizarro, A., Dal Sasso, S. F., Perks, M. T., and Manfreda, S.: Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrol. Earth Syst. Sci., 24, 5173–5185, https://doi.org/10.5194/hess-24-5173-2020, 2020.
Pizarro, A., Dal Sasso, S. F., and Manfreda, S.: VISION: VIdeo StabilisatION
using automatic features selection, OSF [code], https://doi.org/10.17605/OSF.IO/HBRF2, 2021.
Puglisi, G. and Battiato, S.: A robust image alignment algorithm for video
stabilization purposes, IEEE Trans. Circuits Syst. Video Technol., 21,
1390–1400, https://doi.org/10.1109/TCSVT.2011.2162689, 2011.
Rosten, E. and Drummond, T.: Machine Learning for High-Speed Corner Detection, in: Leonardis, A., Bischof, H., and Pinz, A., Computer Vision – ECCV 2006, ECCV 2006, Lecture Notes in Computer Science, vol 3951, Springer, Berlin, Heidelberg, https://doi.org/10.1007/11744023_34, 2006.
Shen, H., Pan, Q., Cheng, Y., and Yu, Y.: Fast video stabilization algorithm
for UAV, in IEEE International Conference on Intelligent Computing and
Intelligent Systems, IEEE, 542–546,
https://doi.org/10.1109/ICICISYS.2009.5357609, 2009.
Shi, J. and Tomasi, C.: Good features to track, in Proceedings of IEEE
Conference on Computer Vision and Pattern Recognition CVPR-94,
IEEE Comput. Soc. Press, 593–600, https://doi.org/10.1109/CVPR.1994.323794, 1994.
Stegagno, P., Basile, M., Bulthoff, H. H., and Franchi, A.: A semi-autonomous
UAV platform for indoor remote operation with visual and haptic feedback, in:
2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE,
3862–3869, https://doi.org/10.1109/ICRA.2014.6907419, 2014.
Strelnikova, D.: Fishstream: Extended PiVlab version with the FFT_CUAS stabilisation tool, Bitbucket [code], available at: https://bitbucket.org/SIENA_Research/fishstream, last access: 21 February 2021.
Strelnikova, D., Paulus, G., Käfer, S., Anders, K.-H., Mayr, P., Mader,
H., Scherling, U., and Schneeberger, R.: Drone-Based Optical Measurements of
Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower
Dams, Remote Sens., 12, 384, https://doi.org/10.3390/rs12030384, 2020.
Tauro, F., Porfiri, M., and Grimaldi, S.: Surface flow measurements from
drones, J. Hydrol., 540, 240–245,
https://doi.org/10.1016/j.jhydrol.2016.06.012, 2016.
Thielicke, W. and Stamhuis, E. J.: PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB, J. Open Res. Softw., 2, e30, https://doi.org/10.5334/jors.bl, 2014.
Thillainayagi, R. and Senthil Kumar, K.: Video stabilization technique for thermal infrared Aerial surveillance, Online International Conference on Green Engineering and Technologies (IC-GET), 2016, https://doi.org/10.1109/GET.2016.7916630, 2016.
Wang, Y., Hou, Z. J., Leman, K., and Chang, R.: Real-time video stabilization
for Unmanned Aerial Vehicles, Proc. 12th IAPR Conf. Mach. Vis. Appl. MVA
2011, 336–339, 2011.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.: Image Quality
Assessment: From Error Visibility to Structural Similarity, IEEE Trans.
Image Process., 13, 600–612, https://doi.org/10.1109/TIP.2003.819861,
2004.
Yang, J., Schonfeld, D., and Mohamed, M.: Robust video stabilization based on
particle filter tracking of projected camera motion, IEEE Trans. Circuits
Syst. Video Technol., 19, 945–954,
https://doi.org/10.1109/TCSVT.2009.2020252, 2009.
Short summary
The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.
The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed...
